Shared Execution of Clustering Tasks.

Clustering is a central problem in non-relational data analysis, with k-means being the most popular clustering technique. In various scenarios, it may be necessary to perform clustering over the same input data multiple times - with different values of k, different clustering attributes, or different initial centroids - before arriving at the final solution. In this paper, we propose algorithms for parallel execution of multiple runs of k-means clustering in a way that achieves substantial savings of IO and processing resources. Proposed algorithms can easily be implemented over Hadoop/MapReduce, Spark, etc., with savings in map and reduce phases. Extensive performance evaluation using real-world datasets show that the proposed algorithms result in up to 40% savings in response times when compared to other optimization techniques proposed in literature as well as open-source implementations. The algorithms scale well with increasing data sizes, values of k, and number of clustering tasks.